import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__  =					get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_sentencepiece
@require_tokenizers
class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					XLMRobertaTokenizer
 __A			       =					XLMRobertaTokenizerFast
 __A			       =					True
 __A			       =					True







 def 		__UpperCAmelCase				(		self				:   str)						->	Optional[Any]:


   """simple docstring"""







   super().setUp()

   # We have a SentencePiece fixture for testing
   _UpperCamelCase           =    XLMRobertaTokenizer(lowercase_							, keep_accents=lowercase_)
   tokenizer.save_pretrained(self.tmpdirname)







 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    "<pad>"
   _UpperCamelCase           =    1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_)							, lowercase_)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_)							, lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    list(self.get_tokenizer().get_vocab().keys())

   self.assertEqual(vocab_keys[0]							, "<s>")
   self.assertEqual(vocab_keys[1]							, "<pad>")
   self.assertEqual(vocab_keys[-1]							, "<mask>")
   self.assertEqual(len(lowercase_)							, 1002)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Optional[Any]:


   """simple docstring"""







   self.assertEqual(self.get_tokenizer().vocab_size							, 1002)







 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    XLMRobertaTokenizer(lowercase_							, keep_accents=lowercase_)

   _UpperCamelCase           =    tokenizer.tokenize("This is a test")
   self.assertListEqual(lowercase_							, ["▁This", "▁is", "▁a", "▁t", "est"])

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(lowercase_)							, [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]							, )

   _UpperCamelCase           =    tokenizer.tokenize("I was born in 92000, and this is falsé.")
   self.assertListEqual(
       lowercase_							, [
           SPIECE_UNDERLINE + "I",
           SPIECE_UNDERLINE + "was",
           SPIECE_UNDERLINE + "b",
           "or",
           "n",
           SPIECE_UNDERLINE + "in",
           SPIECE_UNDERLINE + "",
           "9",
           "2",
           "0",
           "0",
           "0",
           ",",
           SPIECE_UNDERLINE + "and",
           SPIECE_UNDERLINE + "this",
           SPIECE_UNDERLINE + "is",
           SPIECE_UNDERLINE + "f",
           "al",
           "s",
           "é",
           ".",
       ]							, )
   _UpperCamelCase           =    tokenizer.convert_tokens_to_ids(lowercase_)
   self.assertListEqual(
       lowercase_							, [
           value + tokenizer.fairseq_offset
           for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
       ]							, )

   _UpperCamelCase           =    tokenizer.convert_ids_to_tokens(lowercase_)
   self.assertListEqual(
       lowercase_							, [
           SPIECE_UNDERLINE + "I",
           SPIECE_UNDERLINE + "was",
           SPIECE_UNDERLINE + "b",
           "or",
           "n",
           SPIECE_UNDERLINE + "in",
           SPIECE_UNDERLINE + "",
           "<unk>",
           "2",
           "0",
           "0",
           "0",
           ",",
           SPIECE_UNDERLINE + "and",
           SPIECE_UNDERLINE + "this",
           SPIECE_UNDERLINE + "is",
           SPIECE_UNDERLINE + "f",
           "al",
           "s",
           "<unk>",
           ".",
       ]							, )







 def 		__UpperCAmelCase				(		self				:   Any)						->	Dict:


   """simple docstring"""







   if not self.test_slow_tokenizer:
     # as we don't have a slow version, we can't compare the outputs between slow and fast versions
     return

   _UpperCamelCase           =    (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
       _UpperCamelCase           =    self.rust_tokenizer_class.from_pretrained(lowercase_							, **lowercase_)
       _UpperCamelCase           =    self.tokenizer_class.from_pretrained(lowercase_							, **lowercase_)

       _UpperCamelCase           =    tempfile.mkdtemp()

       _UpperCamelCase           =    tokenizer_r.save_pretrained(lowercase_)
       _UpperCamelCase           =    tokenizer_p.save_pretrained(lowercase_)

       # Checks it save with the same files + the tokenizer.json file for the fast one
       self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
       _UpperCamelCase           =    tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
       self.assertSequenceEqual(lowercase_							, lowercase_)

       # Checks everything loads correctly in the same way
       _UpperCamelCase           =    tokenizer_r.from_pretrained(lowercase_)
       _UpperCamelCase           =    tokenizer_p.from_pretrained(lowercase_)

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
         self.assertTrue(hasattr(lowercase_							, lowercase_))
         # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
         # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

       shutil.rmtree(lowercase_)

       # Save tokenizer rust, legacy_format=True
       _UpperCamelCase           =    tempfile.mkdtemp()

       _UpperCamelCase           =    tokenizer_r.save_pretrained(lowercase_							, legacy_format=lowercase_)
       _UpperCamelCase           =    tokenizer_p.save_pretrained(lowercase_)

       # Checks it save with the same files
       self.assertSequenceEqual(lowercase_							, lowercase_)

       # Checks everything loads correctly in the same way
       _UpperCamelCase           =    tokenizer_r.from_pretrained(lowercase_)
       _UpperCamelCase           =    tokenizer_p.from_pretrained(lowercase_)

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
         self.assertTrue(hasattr(lowercase_							, lowercase_))

       shutil.rmtree(lowercase_)

       # Save tokenizer rust, legacy_format=False
       _UpperCamelCase           =    tempfile.mkdtemp()

       _UpperCamelCase           =    tokenizer_r.save_pretrained(lowercase_							, legacy_format=lowercase_)
       _UpperCamelCase           =    tokenizer_p.save_pretrained(lowercase_)

       # Checks it saved the tokenizer.json file
       self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))

       # Checks everything loads correctly in the same way
       _UpperCamelCase           =    tokenizer_r.from_pretrained(lowercase_)
       _UpperCamelCase           =    tokenizer_p.from_pretrained(lowercase_)

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
         self.assertTrue(hasattr(lowercase_							, lowercase_))

       shutil.rmtree(lowercase_)







 @cached_property
 def 		__UpperCAmelCase				(		self				:   Any)						->	Optional[int]:


   """simple docstring"""







   return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base")







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	int:


   """simple docstring"""







   with tempfile.NamedTemporaryFile() as f:
     shutil.copyfile(lowercase_							, f.name)
     _UpperCamelCase           =    XLMRobertaTokenizer(f.name							, keep_accents=lowercase_)
     _UpperCamelCase           =    pickle.dumps(lowercase_)
   pickle.loads(lowercase_)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Tuple:


   """simple docstring"""







   if not self.test_rust_tokenizer:
     return

   _UpperCamelCase           =    self.get_tokenizer()
   _UpperCamelCase           =    self.get_rust_tokenizer()

   _UpperCamelCase           =    "I was born in 92000, and this is falsé."

   _UpperCamelCase           =    tokenizer.tokenize(lowercase_)
   _UpperCamelCase           =    rust_tokenizer.tokenize(lowercase_)
   self.assertListEqual(lowercase_							, lowercase_)

   _UpperCamelCase           =    tokenizer.encode(lowercase_							, add_special_tokens=lowercase_)
   _UpperCamelCase           =    rust_tokenizer.encode(lowercase_							, add_special_tokens=lowercase_)
   self.assertListEqual(lowercase_							, lowercase_)

   _UpperCamelCase           =    self.get_rust_tokenizer()
   _UpperCamelCase           =    tokenizer.encode(lowercase_)
   _UpperCamelCase           =    rust_tokenizer.encode(lowercase_)
   self.assertListEqual(lowercase_							, lowercase_)







 @slow
 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    "Hello World!"
   _UpperCamelCase           =    [0, 35378, 6661, 38, 2]
   # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
   # xlmr.eval()
   # xlmr.encode(symbols)

   self.assertListEqual(lowercase_							, self.big_tokenizer.encode(lowercase_))







 @slow
 def 		__UpperCAmelCase				(		self				:   List[str])						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    (
       "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
       " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
   )
   _UpperCamelCase           =    [
       0,
       3293,
       83,
       10,
       4552,
       4989,
       7986,
       678,
       10,
       5915,
       111,
       179459,
       124850,
       4,
       6044,
       237,
       12,
       6,
       5,
       6,
       4,
       6780,
       705,
       15,
       1388,
       44,
       378,
       10114,
       711,
       152,
       20,
       6,
       5,
       22376,
       642,
       1221,
       15190,
       34153,
       450,
       5608,
       959,
       1119,
       57702,
       136,
       186,
       47,
       1098,
       29367,
       47,
       # 4426, # What fairseq tokenizes from "<unk>": "_<"
       # 3678, # What fairseq tokenizes from "<unk>": "unk"
       # 2740, # What fairseq tokenizes from "<unk>": ">"
       3,  # What we tokenize from "<unk>": "<unk>"
       6,  # Residue from the tokenization: an extra sentencepiece underline
       4,
       6044,
       237,
       6284,
       50901,
       528,
       31,
       90,
       34,
       927,
       2,
   ]
   # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
   # xlmr.eval()
   # xlmr.encode(symbols)

   self.assertListEqual(lowercase_							, self.big_tokenizer.encode(lowercase_))







 @slow
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    {"input_ids": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
   # fmt: on

   self.tokenizer_integration_test_util(
       expected_encoding=lowercase_							, model_name="xlm-roberta-base"							, revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"							, )





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					['''image_processor''', '''tokenizer''']
 __A			       =					'''ViTImageProcessor'''
 __A			       =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')







 def __init__(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]=None							, lowercase_				:   Dict=None							, **lowercase_				:   List[str])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."							, lowercase_							, )
     _UpperCamelCase           =    kwargs.pop("feature_extractor")

   _UpperCamelCase           =    image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`.")
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`.")

   super().__init__(lowercase_							, lowercase_)







 def __call__(		self				:   Any							, lowercase_				:   List[str]=None							, lowercase_				:   int=None							, lowercase_				:   Optional[Any]=None							, lowercase_				:   Dict=None							, **lowercase_				:   Dict)						->	int:


   """simple docstring"""







   if text is None and visual_prompt is None and images is None:
     raise ValueError("You have to specify either text, visual prompt or images.")

   if text is not None and visual_prompt is not None:
     raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt.")

   if text is not None:
     _UpperCamelCase           =    self.tokenizer(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if images is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None and images is not None:
     _UpperCamelCase           =    {
         "pixel_values": image_features.pixel_values,
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   elif text is not None and images is not None:
     _UpperCamelCase           =    image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   elif visual_prompt is not None:
     _UpperCamelCase           =    {
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   else:
     return BatchEncoding(data=dict(**lowercase_)							, tensor_type=lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, *lowercase_				:   List[Any]							, **lowercase_				:   List[Any])						->	int:


   """simple docstring"""







   return self.tokenizer.batch_decode(*lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, *lowercase_				:   Any							, **lowercase_				:   int)						->	Optional[int]:


   """simple docstring"""







   return self.tokenizer.decode(*lowercase_							, **lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	str:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							, lowercase_							, )
   return self.image_processor_class







 @property
 def 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							, lowercase_							, )
   return self.image_processor




